Fit and sizing guidance
Ask about measurements, preferences, past purchases, return reasons, and product-specific guidance before recommending a size or fit.
Product recommendation AI
Give product guidance that uses context, not keyword matching
Valiopt helps shoppers and support teams answer fit, compatibility, use-case, inventory, and preference questions with recommendations grounded in a live, indexed view of your catalog and policies.
Live Shopify sync
Product updates in Shopify can be synced and indexed so recommendations stay current without polling Shopify directly during each conversation.
Fast retrieval
Valiopt runs product search and recommendation logic in house, which makes the experience faster and more performant than querying Shopify live.
More customizable
Recommendations can weave in product logic, merchandising rules, compatibility data, and other context that may not live in Shopify.
Workflow coverage
Where recommendation AI should be more useful
Compatibility checks
Use model numbers, dimensions, use cases, materials, accessories, or setup details when the customer needs something that works with what they own.
Inventory-aware alternatives
Recommend options that are actually available, in the right variant, and aligned with delivery or budget constraints.
Replacement suggestions
When a return or exchange is likely, guide customers to a better product instead of ending the conversation at refund eligibility.
Policy-safe selling
Avoid medical, legal, warranty, safety, or regulated claims that your team would not allow a human agent to make.
Agent assist
Give human agents a short recommendation rationale, including what constraints the customer gave and which products were excluded.
Data layer
How Valiopt makes recommendations faster and more flexible
Sync and index product changes
When product details change in Shopify, Valiopt can sync and index those updates so the assistant searches a current, optimized product layer.
Avoid slow live polling
Instead of calling Shopify directly for every recommendation, Valiopt can retrieve from its own indexed catalog for lower latency and more reliable performance.
Blend in non-Shopify data
We can add compatibility tables, fit rules, merchandising priorities, support history, product education, or internal notes that are not stored in Shopify.
Recommendation logic
How Valiopt makes recommendations defensible
Structure product knowledge
We identify the product attributes, category rules, compatibility notes, and merchandising priorities that matter for support conversations.
Reason through constraints
The assistant can filter based on the customer's stated need, then explain why an option fits rather than dumping a product list.
Measure recommendation quality
We look at follow-up questions, conversions, returns, escalations, and agent feedback to improve the guidance over time.
Guardrails
Do not let recommendations overreach
Good product guidance is specific without pretending the assistant knows things it cannot know from the data available.
Medical, safety, or regulated claims
Warranty promises and unsupported compatibility
Unavailable variants or stale inventory
Personal data the customer did not provide
Conflicts with merchandising priorities
High-stakes technical recommendations
Related
Want product guidance that sounds like your best support rep?
Send us a sample category, product feed, and common pre-sale questions. We can show how the assistant would reason through recommendations.